https://blog.superhyydl.org/tags/%E8%B0%B1%E7%B3%BB%E5%8F%AF%E5%A1%91%E6%80%A7/Recent content in 谱系可塑性 on Superhyydl's BlogHugozh-cnTue, 01 Sep 2026 00:00:00 +0000https://blog.superhyydl.org/reading/kmt2d-loss-drives-adeno-to-squamous-transition/Tue, 01 Sep 2026 00:00:00 +0000https://blog.superhyydl.org/reading/kmt2d-loss-drives-adeno-to-squamous-transition/<h2 id="一句话亮点">一句话亮点</h2> <p>今天这篇Cell Death &amp; Differentiation的文章揭示了：KMT2D缺失通过重塑染色质景观，驱动肺腺癌向鳞状细胞癌转变，同时意外地让肿瘤对AURKA抑制剂变得格外敏感。</p> <h2 id="背景痛点">背景/痛点</h2> <p>第三代EGFR靶向药（如奥希替尼）已经大大改善了EGFR突变肺癌患者的预后。但靶向药耐药依然是临床上的棘手问题。很多患者耐药后，肿瘤病理类型从腺癌转变成鳞癌——这个现象就叫腺-鳞癌转变。</p> <p>这意味着：肿瘤不是简单地获得了新的基因突变，而是换了个活法——从依赖某个癌基因，变成依赖另一种细胞身份存活。你继续给原来的靶向药，它不吃这套了。</p> <p>那么，谁来主导这个&quot;身份切换&quot;？肿瘤怎么就从一种细胞类型变成另一种了？之前的研究找到了一些线索，比如LKB1缺失可以促进腺-鳞转变。但调控这个过程的表观遗传开关到底是什么，大家心里没底。</p> <p>这篇文章瞄准的就是这个问题。</p> <h2 id="推理链分步拆解">推理链分步拆解</h2> <h3 id="第一步大范围筛锚定kmt2d">第一步：大范围筛，锚定KMT2D</h3> <p>作者来了一个多组学交叉筛选。他们把四组数据放在一起取交集：1）对吉非替尼/奥希替尼耐药的细胞；2）鳞癌相关基因表达高的细胞；3）CCLE细胞系里跟鳞癌打分负相关的表观因子；4）TCGA病人样本里跟鳞癌打分负相关的表观因子。</p> <p>六根&quot;红线&quot;汇成一股，其中就有KMT2D。</p> <p><img alt="Fig. 1：KMT2D low expression is associated with tyrosine kinase inhibitor (TKI) resistance and squamous phenotype transition. A Venn diagram showing the intersection of four datasets: genes negatively correlated with osimertinib IC50 (blue, n = 79, P &lt; 0.05), gefitinib IC50 (red, n = 69, P &lt; 0.05), genes negatively correlated with ssGSEA-derived squamous signature scores in CCLE lung cancer cell lines (green, n = 586, P &lt; 0.05), and negatively correlated with ssGSEA-derived squamous signature scores in TCGA LUAD and LUSC tumors (yellow, n = 331, P &lt; 0.05). A total of six genes, including KMT2D, were commonly identified across all datasets. B Pearson correlation between KMT2D protein expression and osimertinib IC50 in 35 NSCLC cell lines (r = −0.4245, P &lt; 0.0110). C Left: Correlation analysis between KMT2D mRNA expression and the ssGSEA squamous signature score in CCLE NSCLC cell lines (n = 172, r = −0.4488, P &lt; 0.0001). Right: Correlation analysis between KMT2D mRNA expression and the ssGSEA squamous signature score in TCGA lung cancer samples (n = 965; LUAD and LUSC; r = −0.1181, P &lt; 0.0002). D Heatmap showing RNA-seq analysis of gene expression changes in xenograft tumors from mice treated with vehicle control or osimertinib (5 mg/kg) for 2 weeks (datasets from GSE165019). E Heatmap illustrating RNA-seq-based analysis of gene expression profiles from patient tumor samples collected pre- and post-treatment with EGFR tyrosine kinase inhibitors (TKIs). Each row represents a patient sample pair, and genes highlighted in red denote key squamous markers significantly altered following EGFR-TKI treatment (datasets from GSE165019). F Representative immunohistochemistry (IHC) images of xenograft tumors derived from PC9 parental (Par.) and osimertinib- resistant (OR) cells stained for KMT2D, p63, and SOX2. Scale bar, 100 µm. Right: Quantification of marker expression (n = 4). Two-tailed unpaired t-tests, P &lt; 0.01, P &lt; 0.001. G Representative immunostaining of indicated proteins in two paired human EGFR-mutant lung cancer specimens experiencing squamous transition after EGFR TKI failure (pre-1st biopsy vs. post-transition 2nd biopsy). Scale bar, 50 µm. H Violin plots showing the expression of squamous-associated genes in NSCLC cell lines from the CCLE, stratified by KMT2D mutation status (wild-type versus mutant). Two tailed t-test for Mut vs WT per gene (P &lt; 0.05, P &lt; 0.01, P &lt; 0.001). I Kaplan–Meier survival curves were generated to assess the relationship between KMT2D and TP63 expression levels and the overall survival probability in lung cancer patients." loading="lazy" src="https://blog.superhyydl.org/images/reading/kmt2d-loss-drives-adeno-to-squamous-transition/figure-01.png"></p>